1. Field of the Invention
The present invention relates to a method for retrieving a tumor contour of an image processing system and, more particularly, to a method for retrieving a tumor contour of an image processing system that includes a memory storing a grayscale image and a processor.
2. Description of Related Art
Owing to the non-invasive image-information technique, ultrasonography becomes one of the most acceptable medical tools without serious side effect, and an important radiography for retrieving information from patients applied in clinical diagnosis and medical application. Moreover, since advanced ultrasonography can provide real-time medical imaging with high resolution, it is widely applied in medical examination and diagnosis of tumoral changes.
For example, by way of analyzing the ultrasonographic image in detail, the properties of the tumor can be easily identified by doctors. Thus, ultrasonography can advantageously reduce frequency of tumoral biopsy. In ultrasonography, an imaging contour of a tumor is a principal index for the diagnosis of benignancy and malignancy. If the imaging contour of a tumor obtained from ultrasonography can approximate the real appearance of the tumor, it is beneficial to promote the accuracy of the examination of tumors at the initial stage. In clinical researches, professional doctors often provide the diagnosis of a tumor through description or checklist after the ultrasonographic images are output and checked. However, referring to the same image of the tumor, diagnostic variation between different diagnoses of different doctors still occur.
Therefore, as the technique of computer-aided diagnosis and reading-out of tumor image develops, the identification of tumors assisted by a computer is gradually accomplished in clinical application. Basically, the prerequisite of the examination or identification of tumors is to determine the site of an imaged tumor, i.e. to determine the contour of the imaged tumor. Conventionally, the researchers delineate an imaged tumor to determine the contour of the tumor. However, the definition of the image, the recognition of the researchers for the boundary of the tumor, and the operational condition of the researchers all incur the variation of the determination of the contour of the tumor, and then undesirably affect the examination or identification of the tumor.
One of the advantages for diagnosing the contour of a tumor aided by a computer is that most of the people who know the location of the imaged tumor can depict similar or even the same contour of the tumor. In other words, people are not required to carefully delineate a tumor but only approximately depict the boundary of the tumor with naked eyes. Then, the computer can provide the real boundary of the tumor by algorithm. Hence, the computer-aided diagnosis is invariably highlighted in the research of medical image processing. For example, in 1987, Michael Kass et al. set forth Snake algorithm, in which an initial boundary was determined first and then an optimal boundary was found out sequentially by algorithm. Therefore, Snake algorithm now becomes one of the well-known methods in medical image processing.
Snake algorithm, also called “active contour method”, currently becomes the most widely used algorithm in the research of medical image processing. In Snake algorithm, the principal step is to find out delineation of a region with the minimal constraint of the outside region to the delineated region and the minimal influence of the inside image to the delineated region. Owing to the minimal constraint and influence, the delineated region appears as movement of a snake, and performs expansion and contraction.
Snake algorithm can automatically search data in neighboring regions, locally consider data in each region, and retrieve data according to a feature by surrounding a region based on spatial consecution. The advantage illustrated above is the reason why Snake algorithm is called an “active contour method”. Snake algorithm is suitable to retrieve data from a line segment, a boundary, and a contour, to dynamically track, and to three-dimensionally comparison. As long as the initially delineated region is near to the contour of the interesting image, Snake algorithm can propose a final contour by recursive calculation. As shown in FIG. 1, the contour of the airplane is retrieved by the conventional Snake algorithm.
When Snake algorithm is applied in an image having a clear boundary, especially in an image having high contrast difference between outside and inside neighborhoods of the boundary, an acceptable result can be obtained. However, the tumor image often has an indistinct boundary or hypoechoicity, or even has no hypoechoicity such that the applied Snake algorithm needs to be modified to depict the contour clearly. Generally, Snake algorithm is applied to investigate the consecution, curvature, and local gradient energy of each point in the regions (n×n mask) surrounding an predetermined point. However, if the regions surrounding the predetermined point are indistinct, the search result of the regions surrounding the predetermined point is poor in Snake algorithm. If most regions surrounding the main predetermined points are indistinct, Snake algorithm cannot propose acceptable results. On the other hand, if calculating regions surrounding the predetermined point is expanded, Snake algorithm consumes long calculating time and still provides unsatisfactory results for large-scaled indistinct images. Furthermore, the calculation of Snake algorithm requires a depicted contour of an initial region given by the researchers. Once the initial region is improperly determined and the image is indistinct, Snake algorithm cannot output an outstanding result in the ultrasonographic imaging of a tumor.
In conclusion, the conventional delineation of a tumor needs to be carefully made by hands of medical professionals. By contrast, Snake algorithm requires an initial approximate contour of a clear predetermined region instead of delineation by hand, and then a subsequent calculation performs the approaching of the real contour in Snake algorithm. Nevertheless, if the input initial contour depicted by hand is required to very close to the actual contour of the predetermined region, it is time-consuming delineation is inevitable. On the other hand, if the input initial contour is quite different from the contour of the predetermined region, the calculation of the approaching is time-consuming since Snake algorithm falls into no boundary calculation at the same time. Hence, Snake algorithm is not suitable to be applied in an image which is not clear.